Environmental Policy Overlays and Urban Pollution and Carbon Reduction—Evidence from China
Abstract
:1. Introduction
2. Theoretical and Research Hypothesis
2.1. Environmental Policy Synergies
2.2. Analysis of Environmental Policy Coordination Mechanism
3. Study Design
3.1. Data
3.2. Models
3.3. Variable Description
4. Empirical Analysis
4.1. Test for Mixed Effects of Environmental Policies
4.2. Testing the Synergistic Effects of Environmental Policies
4.3. Robustness Tests
4.3.1. Remove Policy Interference
4.3.2. Substitution of Explanatory Variables
4.3.3. Propensity Score Matching
4.3.4. Addressing Endogenous Issues
5. Further Discussion
5.1. Superimposed Effect
5.2. Complementary Effects
5.3. Heterogeneity Test
5.3.1. Age of Principal Officials
5.3.2. Financial Pressure
6. Conclusions and Policy Recommendations
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Du, M.; Wu, F.; Ye, D.; Zhao, Y.; Liao, L. Exploring the effects of energy quota trading policy on carbon emission efficiency: Quasi-experimental evidence from China. Energy Econ. 2023, 124, 106791. [Google Scholar] [CrossRef]
- Wei, Y.; Du, M.; Huang, Z. The effects of energy quota trading on total factor productivity and economic potential in industrial sector: Evidence from China. J. Clean. Prod. 2024, 445, 141227. [Google Scholar] [CrossRef]
- Yu, X.; Wan, K.; Du, Q. Can carbon market policies achieve a “point-to-surface” effect?—Quasi-experimental evidence from China. Energy Policy 2023, 183, 113803. [Google Scholar] [CrossRef]
- Zhang, B.; Chen, X.; Guo, H. Does central supervision enhance local environmental enforcement? Quasi-experimental evidence from China. J. Public Econ. 2018, 164, 70–90. [Google Scholar] [CrossRef]
- Gavard, C.; Winchester, N.; Paltsev, S. Limited trading of emissions permits as a climate cooperation mechanism? US–China and EU–China examples. Energy Econ. 2016, 58, 95–104. [Google Scholar] [CrossRef]
- Jia, K.; Chen, S. Could campaign-style enforcement improve environmental performance? Evidence from China’s central environmental protection inspection. J. Environ. Manag. 2019, 245, 282–290. [Google Scholar] [CrossRef] [PubMed]
- Lu, J. Can the central environmental protection inspection reduce transboundary pollution? Evidence from river water quality data in China. J. Clean. Prod. 2022, 332, 130030. [Google Scholar] [CrossRef]
- Nie, H.; Jiang, M.; Wang, X. The impact of political cycle: Evidence from coalmine accidents in China. J. Comp. Econ. 2013, 41, 995–1011. [Google Scholar] [CrossRef]
- Li, G.; He, Q.; Shao, S.; Cao, J. Environmental non-governmental organizations and urban environmental governance: Evidence from China. J. Environ. Manag. 2018, 206, 1296–1307. [Google Scholar] [CrossRef]
- Testa, F.; Iraldo, F.; Frey, M. The effect of environmental regulation on firms’ competitive performance: The case of the building & construction sector in some EU regions. J. Environ. Manag. 2011, 92, 2136–2144. [Google Scholar]
- Essl, F.; Erb, K.H.; Glatzel, S.; Pauchard, A. Climate change, carbon market instruments, and biodiversity: Focusing on synergies and avoiding pitfalls. Wiley Interdiscip. Rev. Clim. Change 2018, 9, e486. [Google Scholar] [CrossRef]
- Klausbruckner, C.; Annegarn, H.; Henneman, L.R.; Rafaj, P. A policy review of synergies and trade-offs in South African climate change mitigation and air pollution control strategies. Environ. Sci. Policy 2016, 57, 70–78. [Google Scholar] [CrossRef]
- Byrne, J.; Hughes, K.; Rickerson, W.; Kurdgelashvili, L. American policy conflict in the greenhouse: Divergent trends in federal, regional, state, and local green energy and climate change policy. Energy Policy 2007, 35, 4555–4573. [Google Scholar] [CrossRef]
- Oestreich, A.M.; Tsiakas, I. Carbon emissions and stock returns: Evidence from the EU Emissions Trading Scheme. J. Bank. Financ. 2015, 58, 294–308. [Google Scholar] [CrossRef]
- Verbruggen, A.; Laes, E.; Woerdman, E. Anatomy of emissions trading systems: What is the EU ETS? Environ. Sci. Policy 2019, 98, 11–19. [Google Scholar] [CrossRef]
- Li, W.; Tsang, E.W.; Luo, D.; Ying, Q. It’s not just a visit: Receiving government officials’ visits and firm performance in China. Manag. Organ. Rev. 2016, 12, 577–604. [Google Scholar] [CrossRef]
- Ilhan, A.; Ozkan, B. Informal institutions and the role of civic activism in environmental policy implementation: A comparative study of Istanbul and Barcelona. J. Environ. Plan. Manag. 2020, 63, 1–19. [Google Scholar]
- Doran, E.; Brown, G. Unpacking informal institutions: Using frame analysis to understand the role of informal institutions in shaping local climate governance. Clim. Policy 2020, 20, 117–130. [Google Scholar]
- Wang, H.; Fan, C.; Chen, S. The impact of campaign-style enforcement on corporate environmental Action: Evidence from China’s central environmental protection inspection. J. Clean. Prod. 2021, 290, 125881. [Google Scholar] [CrossRef]
- Greenstone, M.; He, G.; Jia, R.; Liu, T. Can technology solve the principal-agent problem? Evidence from China’s war on air pollution. Am. Econ. Rev. Insights 2022, 4, 54–70. [Google Scholar] [CrossRef]
- Gatzert, N. The impact of corporate reputation and reputation damaging events on financial performance: Empirical evidence from the literature. Eur. Manag. J. 2015, 33, 485–499. [Google Scholar] [CrossRef]
- Chen, B.; Wu, R. Legal and Policy Pathways of Carbon Finance: Comparative Analysis of the Carbon Market in the EU and China. Eur. Bus. Organ. Law Rev. 2023, 24, 41–68. [Google Scholar] [CrossRef]
- Wooldridge, J. What’s New in Econometrics? Lecture 6: Control functions and related methods. NBER 2007, 6, 420–445. [Google Scholar]
- Kopas, J.; York, E.; Jin, X.; Harish, S.P.; Kennedy, R.; Shen, S.V.; Urpelainen, J. Environmental justice in India: Incidence of air pollution from coal-fired power plants. Ecol. Econ. 2020, 176, 106711. [Google Scholar] [CrossRef]
- Zhang, X.; Chen, X.; Zhang, X. Economic growth, energy consumption, and PM2.5 concentration in China: Empirical evidence from dynamic panel data models. Ecol. Econ. 2019, 158, 45–54. [Google Scholar]
- Cui, J.; Zhang, J.; Zheng, Y. Carbon pricing induces innovation: Evidence from China’s regional carbon market pilots. In AEA Papers and Proceedings; American Economic Association: Nashville, TN, USA, 2018; Volume 108, pp. 453–457. [Google Scholar]
- Mo, J.L.; Agnolucci, P.; Jiang, M.R.; Fan, Y. The impact of Chinese carbon emission trading scheme (ETS) on low carbon energy (LCE) investment. Energy Policy 2016, 89, 271–283. [Google Scholar] [CrossRef]
- Bellemare, M.F.; Masaki, T.; Pepinsky, T.B. Lagged explanatory variables and the estimation of causal effect. J. Politics 2017, 79, 949–963. [Google Scholar] [CrossRef]
- Alesina, A.; Cassidy, T.; Troiano, U. Old and young politicians. Economica 2019, 86, 689–727. [Google Scholar] [CrossRef]
- Kahn, M.E.; Li, P.; Zhao, D. Water pollution progress at borders: The role of changes in China’s political promotion incentives. Am. Econ. J. Econ. Policy 2015, 7, 223–242. [Google Scholar] [CrossRef]
- Li, H.; Zhou, L.A. Political turnover and economic performance: The incentive role of personnel control in China. J. Public Econ. 2005, 89, 1743–1762. [Google Scholar] [CrossRef]
Variables | Obs | Mean | S.D | Min | Max |
---|---|---|---|---|---|
CO2 | 5130 | 5.620 | 1.305 | 0.333 | 9.508 |
PM2.5 | 5130 | 45.333 | 14.942 | 4.315 | 108.526 |
Economic | 5130 | 9.852 | 1.036 | 3.664 | 13.056 |
Industry | 5130 | 0.623 | 0.089 | 0.194 | 0.914 |
FDI | 5130 | 9.299 | 2.016 | 2.565 | 14.947 |
Innovation | 5130 | 3.359 | 1.894 | 0 | 9.888 |
Fin | 5130 | 13.693 | 1.312 | 4.709 | 18.139 |
Population | 5130 | 5.845 | 0.695 | 2.785 | 8.129 |
Temp | 5130 | 14.854 | 5.260 | −1.210 | 26.464 |
Humidity | 5130 | 69.251 | 9.088 | 35.632 | 84.397 |
Rain | 5130 | 1011.010 | 537.805 | 48.714 | 2812.444 |
Green | 5130 | 7.774 | 1.178 | 0.214 | 11.886 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
CO2 | PM2.5 | CO2 | PM2.5 | |
−0.431 *** (0.048) | −5.976 *** (0.202) | −0.101 *** (0.026) | −6.423 *** (0.251) | |
−0.516 *** (0.042) | −6.392 *** (0.381) | −0.119 *** (0.003) | −6.849 *** (0.359) | |
−0.723 *** (0.022) | −3.824 *** (0.240) | −0.093 *** (0.022) | −0.578 ** (0.245) | |
Cons | 8.277 *** (0.124) | 20.061 *** (1.699) | 1.879 ** (0.803) | 13.796 * (8.225) |
Control | NO | NO | YES | YES |
City–Year Fixed | YES | YES | YES | YES |
0.505 | 0.587 | 0.512 | 0.518 | |
Obs | 5130 | 5130 | 5130 | 5130 |
(1) | (2) | (3) | (4) | (5) | (6) | |
---|---|---|---|---|---|---|
CO2 | PM2.5 | CO2 | PM2.5 | CO2 | PM2.5 | |
−0.102 *** (0.028) | −6.526 *** (0.269) | −0.101 *** (0.026) | −6.433 *** (0.252) | −0.084 ** (0.034) | −6.289 *** (0.297) | |
−0.112 *** (0.021) | −6.931 *** (0.504) | −0.145 *** (0.034) | −6.983 *** (0.382) | −0.118 *** (0.036) | −6.847 *** (0.459) | |
−0.093 *** (0.022) | −0.575 ** (0.246) | −0.090 *** (0.022) | −0.563 ** (0.249) | −0.088 *** (0.022) | −0.612 ** (0.255) | |
−0.007 *** (0.002) | −0.798 *** (0.154) | |||||
−0.007 *** (0.002) | −0.333 *** (0.015) | |||||
−0.141 *** (0.035) | −0.653 *** (0.121) | |||||
0.512 | 0.618 | 0.513 | 0.518 | 0.513 | 0.517 | |
Obs | 5130 | 5130 | 5130 | 5130 | 5130 | 5130 |
(1) | (2) | (3) | (4) | (5) | (6) | (7) | |
---|---|---|---|---|---|---|---|
CO2 | PM2.5 | SO2 | CO2 | PM2.5 | CO2 | PM2.5 | |
−0.042 *** (0.005) | −5.525 *** (0.421) | −0.899 *** (0.044) | −0.051 *** (0.007) | −4.425 *** (0.614) | |||
−0.049 *** (0.006) | −5.590 *** (0.462) | −0.144 ** (0.057) | −0.035 *** (0.004) | −4.729 *** (0.516) | |||
−0.146 *** (0.032) | −1.395 *** (0.381) | −0.207 ** (0.027) | −0.087 *** (0.012) | −0.503 *** (0.021) | |||
−0.047 *** (0.011) | −4.329 *** (0.370) | ||||||
−0.033 *** (0.005) | −5.147 *** (0.428) | ||||||
−0.103 *** (0.019) | −0.251 *** (0.031) | ||||||
0.599 | 0.512 | 0.525 | 0.567 | 0.697 | 0.339 | 0.448 | |
Obs | 2286 | 2286 | 4189 | 5130 | 5130 | 4841 | 4841 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Financial | CO2 | Concern | CO2 | |
0.022 *** (0.005) | −0.053 *** (0.011) | 2.689 ** (1.226) | −0.057 *** (1.226) | |
Financial | −0.370 ** (0.157) | |||
Concern | −0.003 *** (0.001) | |||
Obs | 5130 | 5130 | 5130 | 5130 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Financial | CO2 | Concern | CO2 | |
0.026 ** (0.009) | −0.086 * (0.049) | 0.835 *** (0.114) | −0.004 *** (0.001) | |
Financial | −0.379 ** (0.157) | |||
Concern | −0.079 *** (0.008) | |||
Obs | 5130 | 5130 | 5130 | 5130 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Financial | CO2 | Concern | CO2 | |
0.010 (0.012) | −0.129 *** (0.033) | −0.692 (0.772) | −0.123 *** (0.033) | |
Financial | −0.394 ** (0.156) | |||
Concern | −0.003 *** (0.000) | |||
Obs | 5130 | 5130 | 5130 | 5130 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Enforce | CO2 | System | CO2 | |
2.097 *** (0.138) | −0.017 *** (0.003) | 0.527 *** (0.090) | −0.055 *** (0.008) | |
Enforce | −0.025 *** (0.007) | |||
System | −0.015 *** (0.004) | |||
Obs | 5130 | 5130 | 5130 | 5130 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Enforce | CO2 | System | CO2 | |
2.451 *** (0.201) | −0.020 *** (0.005) | 0.597 *** (0.105) | −0.069 *** (0.012) | |
Enforce | −0.024 *** (0.007) | |||
System | −0.018 *** (0.003) | |||
Obs | 5130 | 5130 | 5130 | 5130 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Enforce | CO2 | System | CO2 | |
2.447 (3.215) | −0.085 ** (0.037) | 0.780 (0.712) | −0.141 *** (0.034) | |
Enforce | −0.017 ** (0.008) | |||
System | −0.019 * (0.011) | |||
Obs | 5130 | 5130 | 5130 | 5130 |
Age < 50 | 50 ≤ Age ≤ 55 | Age > 50 | |||||||
---|---|---|---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | |
CMPCEPI | −0.024 *** (0.006) | −0.099 *** (0.024) | −0.072 (0.091) | ||||||
−0.016 ** (0.007) | −0.141 * (0.082) | −0.078 (0.253) | −0.101 (0.081) | ||||||
−0.084 *** (0.015) | −0.083 (0.063) | ||||||||
0.539 | 0.575 | 0.537 | 0.617 | 0.517 | 0.613 | 0.734 | 0.736 | 0.735 | |
Obs | 1260 | 1260 | 1260 | 2803 | 2803 | 2803 | 1067 | 1067 | 1067 |
(1) | (2) | (3) | (4) | (5) | (6) | |
---|---|---|---|---|---|---|
Financial = 0 | Financial = 1 | |||||
−0.020 *** (0.004) | 0.003 (0.036) | |||||
−0.017 *** (0.005) | −0.026 (0.038) | |||||
−0.583 ** (0.234) | −0.052 (0.041) | |||||
0.501 | 0.462 | 0.601 | 0.553 | 0.524 | 0.553 | |
Obs | 2412 | 2412 | 2412 | 2718 | 2718 | 2718 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Wan, K.; Bu, Y. Environmental Policy Overlays and Urban Pollution and Carbon Reduction—Evidence from China. Sustainability 2024, 16, 3272. https://doi.org/10.3390/su16083272
Wan K, Bu Y. Environmental Policy Overlays and Urban Pollution and Carbon Reduction—Evidence from China. Sustainability. 2024; 16(8):3272. https://doi.org/10.3390/su16083272
Chicago/Turabian StyleWan, Kai, and Yanjun Bu. 2024. "Environmental Policy Overlays and Urban Pollution and Carbon Reduction—Evidence from China" Sustainability 16, no. 8: 3272. https://doi.org/10.3390/su16083272